A new generation of medical imaging scanners is currently being developed, which combines the high sensitivity of functional imaging by positron emission tomography (PET) with the wide range of imaging and other applications of magnetic resonance (MR) scanners. This new type of scanners not only enables physicians to acquire anatomical data without any extra radiation dose delivered to the patient, which is already an advantage over PET-Computer Tomography (CT), but also reduces the total time needed for performing both functional and anatomical imaging in the case of a true simultaneous scanner. This increases the throughput of PET and MR departments. A PET-MR scanner is also a very useful research tool, enabling researchers, for example, to validate molecular MR imaging protocols against PET, which can be considered the current gold standard in molecular imaging.
Positron emission tomography provides quantitative images depicting the concentration of the positron emitting substance throughout the patient. The accuracy of this quantitative measurement depends in part on the accuracy of an attenuation and scatter correction which accounts for the absorption or scatter of some of the gamma rays as they pass through the patient.
One of the difficulties in designing a multimodality PET-MR scanner is the derivation of that attenuation map to correct the PET image for said attenuation. The necessity of good attenuation correction has been demonstrated in the literature, definitely for the case of quantitative PET imaging. PET scanners use a transmission source integrated in the PET gantry to acquire an attenuation map. PET-CT scanners acquire a CT image and then rescale Hounsfield units to 511 keV linear attenuation coefficients. The small space available inside the bore of a MR scanner renders both options unfeasible. These methods also deliver a significant radiation dose to the patient, which should always be avoided if possible. A suitable method to derive the attenuation map from the MR image is therefore necessary.
The attenuation map reflects the tissue density distribution across the imaging volume. The high density of some biological tissues, such as cortical bone, is mainly caused by atoms with a high atomic number (compared to hydrogen, carbon, oxygen and other abundant atoms in tissue) such as calcium. This density is the direct source of contrast in a CT image, which makes the use of CT for attenuation correction very straightforward. The implicit difficulty in estimating the attenuation map from MR images is that the MR signal has no direct correlation with tissue density, as MR sequences only measure the relaxation of proton spins in a high magnetic field. The contrast in MR images is thus generated by differences in relaxation properties of tissues rather than by their density. The challenge in MR based attenuation correction is finding a way to determine tissue density based on information that bears no direct correlation with this characteristic.
Some methods have been developed for deriving the attenuation map from a magnetic resonance image. They can be divided into two main classes: registration based or segmentation based. The registration based methods start from a MR template and an attenuation map template. The MR template is registered using non-rigid registration to a patient MR image and the same non-rigid transformation is applied to the attenuation map template. This results in a deformed template attenuation map which theoretically predicts the attenuation map that would be acquired with a PET transmission scan of the patient.
Segmentation based methods derive the attenuation map directly from the MR image intensity. Most methods use a two-step approach: first the image is segmented into tissue classes with a known linear attenuation coefficient (mainly bone, soft tissue and air), after which the voxels belonging to a certain tissue class are assigned the corresponding linear attenuation coefficient. However, the very low signal intensity of cortical bone in images acquired with conventional MR sequences makes it difficult to distinguish this tissue type from air, while cortical bone has an attenuation coefficient strongly different from air. Most segmentation based methods therefore also use some kind of anatomic precondition to segment the images. In general, segmentation based methods yield an attenuation map which does not contain a continuous range of attenuation coefficients, but only a number of discrete values, dependent on the number of tissue types distinguished.
In the paper “MRI-based attenuation correction for PET/MRI: a novel approach combining pattern recognition and atlas registration” (Hofmann et al., J Nucl Med, 2008, 49, pp. 1875-1883) a method has recently been proposed which combines pattern recognition and atlas registration to predict the attenuation map. The algorithm is trained by extracting patches out of registered MR and CT data sets and determining a mapping between patches of both modalities. The mapping also uses information from a registered CT which is obtained in the same fashion as in the described registration based methods. The most likely CT-value for every voxel is then derived, yielding a pseudo-CT. The pseudo-CT is then used as input for CT attenuation correction.
A common property of the above-described methods is that the prediction of the attenuation map is somehow based on anatomical reference data. There lies the vulnerability of these methods in clinical practice, since there can be a lot of variability in patient anatomy. One example is the frontal sinus in the skull, the volume of which can range from very small to very large. Other examples are patients with amputated limbs or patients with severe head trauma. A method which does not rely on anatomical reference data would be applicable to all patients without further conditions.
The MR properties of materials like e.g. biological tissue are determined by two relaxation time constants: T1 for longitudinal relaxation and T2 for transverse relaxation of the proton spins. T1 is always larger than T2. Contrast in MRI is generated by the different relaxation properties of tissue types (e.g. normal and pathological tissue). T1 and T2 are large in water. A short T2 relaxation time is found in supporting tissues like tendons, ligaments and cortical bone. Another property that determines the acquired MR signal is the water content or proton density of a tissue. Supporting tissues also contain less water per unit volume.
Ultrashort Time of Echo (UTE) sequences are well known in the art and have also been described in various patent documents. WO2005/026748 relates to systems and techniques for magnetic resonance imaging (MRI) of samples, including components with small T2 values, wherein ultrashort echo times are applied. US2007/255129 also deals with MRI using ultrashort echo times. To a sample, which exhibits long transverse relaxation time (T2) components and short T2 components, a long inversion radio frequency pulse is applied that inverts magnetization of the long T2 components to minimize signals corresponding to the long T2 components. Application US2008/258727 discloses a method for producing a magnetic resonance image using an ultrashort echo time.
Patent document U.S. Pat. No. 6,603,989 deals with T2 contrast in magnetic resonance imaging with gradient echoes. In WO2008/055498 a map of a tissue (or a part thereof or an organ) is determined that displays a certain (clinical or non-clinical) parameter calculated from the originally acquired imaging data. The map can display one or more intrinsic and/or non-intrinsic physical parameters and/or calculated clinical parameters, individually or in combination with other clinical parameters.
In the paper “MR-based Attenuation Correction for PET using an Ultrashort Echo Sequence (UTE) sequence” (Keereman et al., IEEE Nuclear Science Symposium Conference Record, October 2008, pp. 4656-4661) a method for estimating an attenuation map is disclosed that can be used even when the considered patient has non-standard anatomic features. The estimated attenuation map can subsequently be used to correct the PET image for attenuation. The paper demonstrates the importance of taking into account bone for attenuation correction. However, an important drawback of the proposed method is that the attenuation map is derived by using thresholds on the image intensity of the long and short echo image acquired with the UTE sequence. As image intensity in MR is not a stable, quantitative measure (it depends on receiver gain settings etc.), this method would be difficult to implement on a large scale as the thresholds would have to be reconfigured for each imaging session. Consequently, there is a need for a method wherein a quantitative parameter is used to derive the attenuation map.